جستجوی مقالات مرتبط با کلیدواژه « سامانه های اطلاعات مکانی » در نشریات گروه « عمران »

Proper management of epidemic diseases such as Covid-19 is very important because of its effects on the economy, culture and society of nations. By applying various control strategies such as closing schools, restricting night traffic and mass vaccination program, the spread of this disease has been somewhat controlled but not completely stopped. The main goal of this research is to provide a flexible spatio-temporal model for simulating the spread of the Covid-19 disease in order to investigate and evaluate the effectiveness of vaccination. For this purpose, the combination of Agent Based Modelling (ABM) with changeable input parameters and Geospatial Information System (GIS) has been used. The disease spreads through the interaction of the designed agents with each other and with the environment, with the help of the SEIRD epidemic model, and the characteristics of the agents are monitored during the simulation period. To evaluate the model, the real data of patients with the disease in Urmia city from the time of the outbreak to 140 days later were used. The results show that the implemented model simulates the spread of the disease with MAPE= 32.86% and NRMSE= 8.62%. By simulating the vaccination implementation plan, the total number of infected people will decrease by 36.12% and the total number of deaths will decrease by 44.48%. Comparison of simulation outputs and real data shows a similarity of 82% between model results and reality. The result of this research shows that agent based modelling has been able to simulate the spread of the corona virus to an acceptable extent and evaluate the control strategies effectively; Therefore, agent based models can be used to simulate the spread of different variants of the Corona virus and other epidemic diseases, as well as to simulate the environment's response and control strategies.

The occurrence of crime in society causes insecurity and chaos in people's lives, imposes extensive material and spiritual costs on society, and diverts a lot of time, money, and energy from the country's security agents. Spatial investigation of a crime in the past and predicting the likelihood of its occurrence in the future, or in other words, determining the spatial centers of crime, can help manage crime prevention better or deal with it effectively. Unfortunately, measuring a crisis center is a complex issue. In this regard, many statistical methods can be used to identify crisis hotspots, including cluster analysis. In the present study, for the proper efficiency of such analyzes, with a combined approach of spatial information systems and K-means clustering, the spatial distribution of crime in Qazvin (as a case study) is investigated. Assist in locating crime centers more accurately. The results showed that this combined approach could model the local centers of crime in Qazvin.

With the development of complex urban infrastructure such as high-rise buildings, the importance of geospatial information in the interior of buildings becomes more apparent. Routing is one of the most basic processes related to geospatial information that existence of a precise and complete network of the desired environment is necessary for its efficiency. In most cases, a data set from the target network is required before performing any analysis on a route network; Having such data outside the building, such as Google Map and OSM data, as well as accessing it is much easier than inside the building. Access to such information requires geometric information and in some cases accurate semantic information of the building. In this regard, building information modeling technology (BIM), by providing useful information about buildings can help to build a route network in its interior. On the other hand, in order to complete the routing process, in addition to the route network, efficient tools for analysis on the route network are needed, which are not available in BIM. In this regard, Spatial Information Systems (GIS) have an ability to perform the required analysis in the routing process. Therefore, it seems that by combining BIM and GIS, the ground can be provided for better management of the routing process inside the building. In this research, the combination of BIM and GIS has been done with the aim of building an indoor route network using FME Reader software. In this research, the campus of the Faculty of Engineering, University of Tehran has been considered as the study building. In order to prepare a three-dimensional model of these floors, Revit Architecture 2017 software has been used. First, the two-dimensional map related to the plan of these classes is entered into the software environment and then the three-dimensional process is implemented in accordance with the necessary standards. After extracting the required data in the BIM model, this data has become the standard format in GIS. In order to eliminate the errors resulting from the conversion of data format from BIM to GIS, such as overshoot and undershoot errors, concepts and topological analysis in GIS have been used. Finally, the two proposed methods Mesh and TIN have been used to create the route network. The Mesh method uses regular grading of the region to create the route network, while the TIN method uses the Delaunay triangulation concepts to construct the route network. In the Mesh method, the number of points involved in routing is very high. High number of points increases the accuracy of routing, but processing these points in order to find the optimal path requires high hardware. The TIN method uses triangulation to network the environment, and therefore, due to the low number of points involved in the path network, information processing in order to find the best path requires less hardware power. Also in this study, the stairs were connected to each other as a straight line, which reduces the accuracy of routing. Routing can be used in technologies such as in-house positioning. The results of the research show the efficiency of the information in BIM models as well as the proposed methods in constructing the route network in the routing process.

The present study attempts to optimally model the daily temporal traffic accidents on Karaj-Qazvin freeway, as one of Iran's accident-prone roads between 2009-2013, using two methods of Time Series and Artificial Neural Network, based on spatial information system. Temporal patterns of road hazards are obtained from temporal traffic accident data (sample size equals to 1097) by taking into account trend and periodic components, model type and order, Autocorrelation and Partial Autocorrelation Functions. In the former method, the dependence of time domain and the order of temporal model were calculated and in the latter method, different architectures of Multilayer perceptron (MLP) (a Feedforward Artificial Neural Network) were implemented to determine the most optimum diagnostic performance. Characteristic indices, coefficient of determination and accuracy were used to evaluate the network and ARCGIS and MATLAB software were used to calculate the two models in different scenarios. The results indicate that the Artificial Neural Network model, with coefficient of determination and root mean square error 10.71, can estimate the rate of daily accidents somewhat better than the Time Series method and Partial Autocorrelation with coefficient of determination and root mean square error 14.31. It should be noted that the modeling of daily traffic accident data using Artificial Neural Network model and Partial Autocorrelation, has not yet been presented in similar studies and research.

Because in Iran, flood phenomenon has the highest financial losses and death tolls among the natural disasters, thus reducing the damage caused by it has been considered for a long time and it is regarded essential. The most urgent action to deal with this natural disaster is to make preparations and take measures to reduce its harmful effects. One of the basic measures in this regard is the recognition of flood-stricken areas and the zoning of these areas in terms of the risk of flooding or runoff production. As a result, it is possible to decide on the use of land and various applications, such as agriculture, urban, industrial, etc., and minimize flood damages. In this study, first the factors affecting flood formation such as geology, precipitation, drainage density, distance from the waterway, concentration time, slope, aspect, soil material and land use were identified. Then, using the GIS capabilities and the fuzzy hierarchical analytical method, the zoning map of flood gas potential of the Tajan basin was prepared in five classes of very low, low, moderate, high and very high floodwater potential. Accordingly, 0.51% of the basin areas have very high runoff potential, 5.48% of the regions are with low runoff potential, 29.09% are with medium run runoff potential, 52.4% are with high runoff potential and 12.52% are with very high runoff production potential. Also, the range of Sari city is in the range from medium to high runoff potential, and 87 of the 441 villages in the studied area have very high runoff potential and are in the danger zone. In general, the situation of the roads in the basin is not suitable for the runoff production potential, and the main parts of the roads in the catchment area, including the main roads such as the parts of the Sari-Semnan axis, have very high run runoff production potential. Flood Damage alleviation both by structural or Non-Structural measures firstly requires accurate identification of study area in terms of flood risk or runoff production potential. This is more important due to financial limitations and the lack of an integrated management perspective. The presence of a zoning map of runoff production potential in Tajan basin is more important as it is located in border of Mazandaran and Semnan provinces and since some of the flood-prone areas are located in Shirin Rood tributaries at Chashm district of Semnan province and its floods impose substantial damage to downstream areas in Mazandaran province. Hence, this zonation map and its results, i.e. identifying runoff prone areas, assist us to define, complete and develop watershed project at   upstream of flood-prone areas with high runoff potential with holistic view as well as proper prioritization in management decisions. Also through management of the financial and human resources, roads prone to flooding can be secured and it helps to prevent the establishment of industries and population centers and the implementation of civil constructions and projects in the high risk areas. At the same time   in credit and budget allocation for flood and education areas with high runoff generation potential are prioritized. It is also possible to consider the zoning map of runoff production potential in the selection of plowing and furrow methods, tillage and flood protection and flood control measures.

The era of big data is approaching with the rapid growth in development of complex information and communication technologies like internet and 3rd and 4th generation of Mobile Networks (3G/4G). These days more than ever, real-time and concurrent access to heterogeneous data from different sources and with different formats is made possible. Meanwhile, with recent advances in sensor technology and in order to monitor, explore and visualize complex spatial systems, a large amount of data in different Spatio-temporal scales is being generated every day. For example, according to the estimation by United Nations Initiative on Global Geospatial Information Management (UN-GGIM), 2.5 quintillion bytes of data are generated every day which large portion of the data is location-aware. This unprecedented trend of spatial data generation provides new opportunities for information and knowledge extraction, industrial development and business decision making. Although the big data brings new chances for scientific, business and engineering fields, it presents some challenges. To be more specific, storage, management, process and analysis of the spatial big data on traditional spatial information platforms is difficult and expensive. Such challenges affect modeling, analysis, simulation and concurrent access to spatial data. The need of real-time analysis in some applications like dynamic routing, fleet management or emergency management is also influenced by such limitations. In order to face spatial big data challenges, cloud computing technology to support spatial information applications appears to be very promising. Emergence of cloud computing technology provides an effective, scalable and affordable solution to big data challenges in spatial information application. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software. In this paper, we discussed fundamental theories and key technologies of cloud computing in storage, process and analysis of spatial big data. We have surveyed storage and management of big data using distributed file systems and NoSQL databases and made a comparison between different types of this databases. We also discussed recent trends and methods in parallel processing and big data analysis. MapReduce as a prominent parallel programming method and Hadoop as the most popular implementation of MapReduce are surveyed. We reviewed and made a comparison of spatial tools which is developed on cloud platforms. One of the most important challenges in spatial cloud computing that geospatial scientists is facing is spatial indexing and query processing. Due to distributed systems limits, developing dedicated spatial indexing and query processing techniques is needed. So we focused on novel spatial indexing methods and query processing technologies. As the case study, this paper surveys usage of cloud computing technologies in transportation, traffic and Intelligent Transportation Systems (ITS) and remote sensing and earth observations. The aim of this paper is reviewing and introducing fundamental theories, new technologies and recent trends of spatial big data to researchers of geospatial sciences.

In today’s world, rapid and sustainable development is on the top of all countries agenda, including Iran. The major obstacles of sustainable development are climate and environmental conditions. One of the most important climatic constraint in Iran is insufficient rainfall. In addition to world water restrictions, Iran has approximately one third of the average global precipitation. Also the spatial distribution of rainfall due to natural conditions is very heterogeneous and temporal distribution of rainfall as well as the spatial distribution shows a similar trend. For these reasons, the water crisis has become a national predicament. In addition Floods and Droughts are the two faces of the same coin. Over the last few years, the overwhelming majority of disasters have been caused by floods in Iran. So Iran is amongst the few countries that is facing floods and drought simultaneously. Hence, long-term meteorological forecasting is of prime importance and plays a significant role in water resource management and sustainable development. This study presents an approach to forecast the monthly rainfall of north-western part of Iran and produce the spatio-temporal prediction maps in the study area. In this research, precipitation data along with environmental and meteorological information such as minimum monthly temperature, maximum monthly temperature, average monthly temperature, maximum wind speed and mean monthly wind speed from 1950 to 2014 were considered as affecting input parameters. Additionally, topographic parameters, elevation, latitude and longitude were computed from Digital Elevation Mode (DEM). In order to increase the prediction accuracy, large-scale climate data such as North Atlantic Oscillation (NAO), Antarctic Oscillation (AO), Extreme Eastern Tropical Pacific SST (Nino 1), Eastern Tropical Pacific SST (Nino 3), Central Tropical Pacific SST (Nino 4) and East Central Tropical Pacific SST (Nino 3.4), is used along with other environmental and topographical data. Considering the diversity of sources along with amount of the input data, we were facing challenges involving big data storage and processing. Hence for data storage, Cassandra as a NoSQL database was used. The two main reasons to choose a No SQL database are robust and reliable architecture and providing a mechanism for storage and retrieval of data. Then, shallow artificial neural network and deep belief network as two branches of machine learning were trained and tested. Forecasted precipitation maps for twelve’s months of the 2014 were produced afterward using both shallow neural network and deep belief network. In order to evaluate and compare the performances of the networks, four criteria, including accuracy, precision, recall and f1 score, were used. The comparison between monthly precipitation forecast and measured precipitation shows that deep belief network is capable of handling very large spatial-temporal data sets and is also able to solve the complexities of forecasting precipitation. The results indicate that the accuracy of shallow and deep neural networks were 0.67 and 0.71, the precision were 0.69 and 0.69, the recall were 0.7 and 0.8 and the f1 score were 0.69 and 0.74, respectively.

Nowadays, by appearing new science and technologies like ubiquitous computing, pervasive computing, and also Mobile GIS, the dream of creating smart partners for every human has been fairly fulfilled. Although, processing power and speed were two aspects of developing computing tools in the past, these days versatility is the most important goal for inventing new computing devices. So, it can be predicted that future devices will come to daily life of human and so the abstraction process of information and displaying useful ones is one of the main concerns in future Mobile environments. In this study, by considering the basic role of showing spatial information in Mobile environments, we tend to explain different and also important tools for showing information on Mobile devices with Android operating system. Despite the critical importance of mentioned purpose, no comprehensive study has been done in this area yet. In addition, based on each tool, different services will be developed and performance of each method will be experimented and discussed. At the end, by the weighing of pros and cons, all of the advantages and also disadvantages of each method will be explained. In summary, the results of this study can be considered as the starting point for any kind of Mobile GIS projects.

Extraction of spatial relationships between georeferenced layers is one of the important objectives in spatial modeling in Geographic Information Systems (GIS). In last decades, a lot of techniques have been proposed for spatial modeling. Among them, Computational Intelligence techniques have been successfully employed in a wide range of spatial modeling.
Most Computational Intelligence techniques automatically solve problems with requiring the user to know or specify the form or structure of the solution in advance. However, in most cases determining the structure of the solution in advance is difficult and may lead to inaccurate results.
In order to overcome this challenge Genetic Programming (GP) which is a systematic, domain-independent method inspired by evolution is applied. In GP, user is not required to specify the structural complexity of the solution in advance but the algorithm tries to find an explicit relationship between the input and output. GP uses a tree-structure which captures the executional ordering of the functional components within a program: such that a program output appears at the root node; functions are internal tree nodes; a function's arguments are given by its child nodes; and terminal arguments are found at leaf nodes. However, GP can have a tendency to find solutions that are biased towards the training set (Overfitting). In this research we proposed a new method for limiting the effect of overfitting in GP. Also, for achieving more accurate results we use multigene GP in which individual consists of one or more trees.
At the final stage, Sensitivity Analysis (SA) which is the study of how uncertainty in the output of a model can be apportioned to different sources of uncertainty in the model input is used to determine what inputs, parameters or decision variables contribute more to the variance in the output of a model. There are three types of SA: 1-Screening SA, 2-Local SA and 3-Global SA. Screening SA methods are approximate but with low computational cost. When dealing with models containing large amounts of uncertain input factors, screening methods could be useful because they are able to isolate the set of factors with strongest effect on the output variability by very few model evaluations. A drawback of this feature is that the sensitivity measure is only qualitative which means the input factors are ranked in order of importance. Local SA looks at the local impact of each factor on the model output. The input variables are basically changed one at a time and the impact of this individual parameter perturbation on the model output is calculated using local sensitivity indices. A drawback of this feature is that the method does not work when the model is either nonlinear or several input factors are affected by different uncertainties. In global SA, both relative contributions of each individual parameter and the interactions between parameters to the model output variance are simultaneously evaluated by varying all input parameters simultaneously over the entire input parameters space. Several types of global SA, such as partial rank correlation coefficient, multiparametric sensitivity analysis, Fourier amplitude sensitivity analysis (FAST) and Sobol’s method have been used successfully in different models. Among different methods of sensitivity analysis, Sobol’s and EFAST as the methods of variance-based are employed.
The proposed method has several applications in spatial modeling issues. As a case study, the proposed procedures were applied to produce mineral potential map of Aliabad copper deposit. Results indicate that total field intensity criterion has the most effect and lithology has the minimal impact on the mineral potential mapping.